Pandas Profiling Report

Dataset statistics

Number of variables	20
Number of observations	48678
Missing cells	54628
Missing cells (%)	5.6%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	7.4 MiB
Average record size in memory	160.0 B

Variable types

Numeric	8
Categorical	12

Alerts

`Rural_Urban` is highly correlated with `WealthQuantile` and 2 other fields	High correlation
`BirthOrder` is highly correlated with `TotalChildren`	High correlation
`TotalChildren` is highly correlated with `BirthOrder`	High correlation
`WealthQuantile` is highly correlated with `Rural_Urban` and 1 other fields	High correlation
`Toiletfascility` is highly correlated with `Rural_Urban`	High correlation
`Place_of_delivery` is highly correlated with `Rural_Urban`	High correlation
`Mediaexposure` is highly correlated with `WealthQuantile`	High correlation
`Rural_Urban` is highly correlated with `WealthQuantile` and 2 other fields	High correlation
`BirthOrder` is highly correlated with `TotalChildren`	High correlation
`TotalChildren` is highly correlated with `BirthOrder`	High correlation
`WealthQuantile` is highly correlated with `Rural_Urban` and 1 other fields	High correlation
`Toiletfascility` is highly correlated with `Rural_Urban`	High correlation
`Place_of_delivery` is highly correlated with `Rural_Urban`	High correlation
`Mediaexposure` is highly correlated with `WealthQuantile`	High correlation
`Rural_Urban` is highly correlated with `Toiletfascility` and 1 other fields	High correlation
`BirthOrder` is highly correlated with `TotalChildren`	High correlation
`TotalChildren` is highly correlated with `BirthOrder`	High correlation
`Toiletfascility` is highly correlated with `Rural_Urban`	High correlation
`Place_of_delivery` is highly correlated with `Rural_Urban`	High correlation
`Mediaexposure` is highly correlated with `WealthQuantile`	High correlation
`Place_of_delivery` is highly correlated with `Rural_Urban` and 1 other fields	High correlation
`Toiletfascility` is highly correlated with `Rural_Urban` and 1 other fields	High correlation
`Rural_Urban` is highly correlated with `Place_of_delivery` and 2 other fields	High correlation
`WealthQuantile` is highly correlated with `Mediaexposure` and 3 other fields	High correlation
`Region` is highly correlated with `Rural_Urban` and 2 other fields	High correlation
`Rural_Urban` is highly correlated with `Region` and 5 other fields	High correlation
`No_of_Children_under_5` is highly correlated with `dead_alive`	High correlation
`BirthOrder` is highly correlated with `TotalChildren`	High correlation
`TotalChildren` is highly correlated with `BirthOrder`	High correlation
`HighestEducation` is highly correlated with `Rural_Urban` and 3 other fields	High correlation
`WealthQuantile` is highly correlated with `Region` and 1 other fields	High correlation
`Toiletfascility` is highly correlated with `Rural_Urban` and 2 other fields	High correlation
`Place_of_delivery` is highly correlated with `Region` and 3 other fields	High correlation
`Mediaexposure` is highly correlated with `Rural_Urban` and 1 other fields	High correlation
`age` is highly correlated with `dead_alive`	High correlation
`dead_alive` is highly correlated with `No_of_Children_under_5` and 1 other fields	High correlation
`Prec.BirthInterval` has 9915 (20.4%) missing values	Missing
`childSize` has 5719 (11.7%) missing values	Missing
`WealthQuantile` has 10873 (22.3%) missing values	Missing
`prev_termin_pregnancy` has 5719 (11.7%) missing values	Missing
`Toiletfascility` has 1323 (2.7%) missing values	Missing
`HandwashFascility` has 9232 (19.0%) missing values	Missing
`Place_of_delivery` has 6389 (13.1%) missing values	Missing
`Mediaexposure` has 5458 (11.2%) missing values	Missing
`No_of_Children_under_5` has 2073 (4.3%) zeros	Zeros
`age` has 2259 (4.6%) zeros	Zeros

Reproduction

Analysis started	2022-02-14 14:52:20.796553
Analysis finished	2022-02-14 14:52:43.249970
Duration	22.45 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

Region
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	15
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	5.812666913

Minimum	1
Maximum	15
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	3
median	5
Q3	7
95-th percentile	14
Maximum	15
Range	14
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	3.798439528
Coefficient of variation (CV)	0.6534762073
Kurtosis	-0.02606129977
Mean	5.812666913
Median Absolute Deviation (MAD)	2
Skewness	0.9068475744
Sum	282949
Variance	14.42814285
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
4	8171	16.8%
7	6857	14.1%
3	5846	12.0%
1	4775	9.8%
5	4502	9.2%
2	4046	8.3%
6	3924	8.1%
12	1972	4.1%
13	1732	3.6%
15	1667	3.4%
Other values (5)	5186	10.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	4775	9.8%
2	4046	8.3%
3	5846	12.0%
4	8171	16.8%
5	4502	9.2%
6	3924	8.1%
7	6857	14.1%
8	1155	2.4%
9	1046	2.1%
10	746	1.5%

Value	Count	Frequency (%)
15	1667	3.4%
14	1293	2.7%
13	1732	3.6%
12	1972	4.1%
11	946	1.9%
10	746	1.5%
9	1046	2.1%
8	1155	2.4%
7	6857	14.1%
6	3924	8.1%

Rural_Urban
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	380.4 KiB

2	40340
1	8338

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2
2nd row	2
3rd row	2
4th row	2
5th row	2

Common Values

Value	Count	Frequency (%)
2	40340	82.9%
1	8338	17.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2	40340	82.9%
1	8338	17.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

No_of_Children_under_5
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	8
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.759439583

Minimum	0
Maximum	10
Zeros	2073
Zeros (%)	4.3%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	1
median	2
Q3	2
95-th percentile	3
Maximum	10
Range	10
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.8495153355
Coefficient of variation (CV)	0.4828329111
Kurtosis	1.303616425
Mean	1.759439583
Median Absolute Deviation (MAD)	1
Skewness	0.5019738489
Sum	85646
Variance	0.7216763053
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
2	21671	44.5%
1	16921	34.8%
3	6922	14.2%
0	2073	4.3%
4	899	1.8%
5	143	0.3%
6	46	0.1%
10	3	< 0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2073	4.3%
1	16921	34.8%
2	21671	44.5%
3	6922	14.2%
4	899	1.8%
5	143	0.3%
6	46	0.1%
10	3	< 0.1%

Value	Count	Frequency (%)
10	3	< 0.1%
6	46	0.1%
5	143	0.3%
4	899	1.8%
3	6922	14.2%
2	21671	44.5%
1	16921	34.8%
0	2073	4.3%

Age_at_1st_Birth
Real number (ℝ_≥0)

Distinct	34
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.86638728

Minimum	8
Maximum	44
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	8
5-th percentile	14
Q1	16
median	18
Q3	21
95-th percentile	26
Maximum	44
Range	36
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.692454624
Coefficient of variation (CV)	0.1957160409
Kurtosis	1.732410874
Mean	18.86638728
Median Absolute Deviation (MAD)	2
Skewness	0.9666448766
Sum	918378
Variance	13.63422115
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=34)

Value	Count	Frequency (%)
17	5939	12.2%
18	5622	11.5%
19	5493	11.3%
16	5461	11.2%
20	4705	9.7%
15	4189	8.6%
21	3493	7.2%
22	2818	5.8%
14	2197	4.5%
23	1942	4.0%
Other values (24)	6819	14.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
8	1	< 0.1%
9	2	< 0.1%
10	14	< 0.1%
11	119	0.2%
12	545	1.1%
13	975	2.0%
14	2197	4.5%
15	4189	8.6%
16	5461	11.2%
17	5939	12.2%

Value	Count	Frequency (%)
44	2	< 0.1%
40	7	< 0.1%
39	2	< 0.1%
38	12	< 0.1%
37	13	< 0.1%
36	23	< 0.1%
35	31	0.1%
34	39	0.1%
33	67	0.1%
32	116	0.2%

BirthOrder
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	18
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.914848597

Minimum	1
Maximum	18
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	6
95-th percentile	9
Maximum	18
Range	17
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	2.560884426
Coefficient of variation (CV)	0.6541464791
Kurtosis	0.148806264
Mean	3.914848597
Median Absolute Deviation (MAD)	2
Skewness	0.8319685542
Sum	190567
Variance	6.558129043
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=18)

Value	Count	Frequency (%)
1	9841	20.2%
2	8330	17.1%
3	6965	14.3%
4	5951	12.2%
5	5099	10.5%
6	4177	8.6%
7	3207	6.6%
8	2226	4.6%
9	1378	2.8%
10	834	1.7%
Other values (8)	670	1.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	9841	20.2%
2	8330	17.1%
3	6965	14.3%
4	5951	12.2%
5	5099	10.5%
6	4177	8.6%
7	3207	6.6%
8	2226	4.6%
9	1378	2.8%
10	834	1.7%

Value	Count	Frequency (%)
18	1	< 0.1%
17	1	< 0.1%
16	5	< 0.1%
15	9	< 0.1%
14	21	< 0.1%
13	59	0.1%
12	186	0.4%
11	388	0.8%
10	834	1.7%
9	1378	2.8%

Prec.BirthInterval
Real number (ℝ_≥0)

MISSING

Distinct	201
Distinct (%)	0.5%
Missing	9915
Missing (%)	20.4%
Infinite	0
Infinite (%)	0.0%
Mean	37.9238965

Minimum	7
Maximum	258
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	7
5-th percentile	14
Q1	24
median	33
Q3	45
95-th percentile	79
Maximum	258
Range	251
Interquartile range (IQR)	21

Descriptive statistics

Standard deviation	21.70359206
Coefficient of variation (CV)	0.5722933048
Kurtosis	9.435824857
Mean	37.9238965
Median Absolute Deviation (MAD)	10
Skewness	2.333973418
Sum	1470044
Variance	471.0459082
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
25	1245	2.6%
26	1217	2.5%
24	1156	2.4%
23	1136	2.3%
34	1081	2.2%
22	1080	2.2%
27	1074	2.2%
33	1052	2.2%
28	1049	2.2%
29	1027	2.1%
Other values (191)	27646	56.8%
(Missing)	9915	20.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
7	8	< 0.1%
8	32	0.1%
9	126	0.3%
10	239	0.5%
11	337	0.7%
12	412	0.8%
13	510	1.0%
14	523	1.1%
15	453	0.9%
16	503	1.0%

Value	Count	Frequency (%)
258	1	< 0.1%
252	1	< 0.1%
247	1	< 0.1%
241	1	< 0.1%
238	1	< 0.1%
229	1	< 0.1%
228	1	< 0.1%
224	1	< 0.1%
219	1	< 0.1%
216	3	< 0.1%

TotalChildren
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	15
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.771375159

Minimum	0
Maximum	14
Zeros	457
Zeros (%)	0.9%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	2
median	3
Q3	5
95-th percentile	8
Maximum	14
Range	14
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.193097519
Coefficient of variation (CV)	0.5815113657
Kurtosis	0.02821227518
Mean	3.771375159
Median Absolute Deviation (MAD)	1
Skewness	0.696249297
Sum	183583
Variance	4.809676729
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
2	9611	19.7%
3	8242	16.9%
4	7277	14.9%
1	6713	13.8%
5	5877	12.1%
6	4353	8.9%
7	3061	6.3%
8	1723	3.5%
9	796	1.6%
0	457	0.9%
Other values (5)	568	1.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	457	0.9%
1	6713	13.8%
2	9611	19.7%
3	8242	16.9%
4	7277	14.9%
5	5877	12.1%
6	4353	8.9%
7	3061	6.3%
8	1723	3.5%
9	796	1.6%

Value	Count	Frequency (%)
14	3	< 0.1%
13	4	< 0.1%
12	54	0.1%
11	124	0.3%
10	383	0.8%
9	796	1.6%
8	1723	3.5%
7	3061	6.3%
6	4353	8.9%
5	5877	12.1%

HighestEducation
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	380.4 KiB

0	34421
1	10335
2	2924
3	998

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	34421	70.7%
1	10335	21.2%
2	2924	6.0%
3	998	2.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	34421	70.7%
1	10335	21.2%
2	2924	6.0%
3	998	2.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

SexofChild
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	380.4 KiB

1	24943
2	23735

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	2
4th row	2
5th row	1

Common Values

Value	Count	Frequency (%)
1	24943	51.2%
2	23735	48.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	24943	51.2%
2	23735	48.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

childSize
Real number (ℝ_≥0)

MISSING

Distinct	7
Distinct (%)	< 0.1%
Missing	5719
Missing (%)	11.7%
Infinite	0
Infinite (%)	0.0%
Mean	3.048720873

Minimum	1
Maximum	9
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	3
Q3	4
95-th percentile	5
Maximum	9
Range	8
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.313379059
Coefficient of variation (CV)	0.4307967548
Kurtosis	0.4629353603
Mean	3.048720873
Median Absolute Deviation (MAD)	1
Skewness	0.3701592213
Sum	130970
Variance	1.724964552
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
3	16773	34.5%
5	7202	14.8%
2	6720	13.8%
1	6341	13.0%
4	5650	11.6%
8	197	0.4%
9	76	0.2%
(Missing)	5719	11.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	6341	13.0%
2	6720	13.8%
3	16773	34.5%
4	5650	11.6%
5	7202	14.8%
8	197	0.4%
9	76	0.2%

Value	Count	Frequency (%)
9	76	0.2%
8	197	0.4%
5	7202	14.8%
4	5650	11.6%
3	16773	34.5%
2	6720	13.8%
1	6341	13.0%

WealthQuantile
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	5
Distinct (%)	< 0.1%
Missing	10873
Missing (%)	22.3%
Memory size	380.4 KiB

1.0	12085
5.0	7472
2.0	6710
3.0	5962
4.0	5576

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	4.0
2nd row	3.0
3rd row	3.0
4th row	3.0
5th row	3.0

Common Values

Value	Count	Frequency (%)
1.0	12085	24.8%
5.0	7472	15.3%
2.0	6710	13.8%
3.0	5962	12.2%
4.0	5576	11.5%
(Missing)	10873	22.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1.0	12085	32.0%
5.0	7472	19.8%
2.0	6710	17.7%
3.0	5962	15.8%
4.0	5576	14.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

prev_termin_pregnancy
Categorical

MISSING

Distinct	3
Distinct (%)	< 0.1%
Missing	5719
Missing (%)	11.7%
Memory size	380.4 KiB

0.0	38585
1.0	4366
9.0	8

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	0.0
3rd row	0.0
4th row	0.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	38585	79.3%
1.0	4366	9.0%
9.0	8	< 0.1%
(Missing)	5719	11.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	38585	89.8%
1.0	4366	10.2%
9.0	8	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Year
Categorical

Distinct	5
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	380.4 KiB

3	11654
1	10873
4	10571
2	9861
5	5719

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
3	11654	23.9%
1	10873	22.3%
4	10571	21.7%
2	9861	20.3%
5	5719	11.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
3	11654	23.9%
1	10873	22.3%
4	10571	21.7%
2	9861	20.3%
5	5719	11.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Toiletfascility
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	2
Distinct (%)	< 0.1%
Missing	1323
Missing (%)	2.7%
Memory size	380.4 KiB

0.0	39770
1.0	7585

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	0.0
3rd row	0.0
4th row	0.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	39770	81.7%
1.0	7585	15.6%
(Missing)	1323	2.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	39770	84.0%
1.0	7585	16.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

HandwashFascility
Categorical

MISSING

Distinct	2
Distinct (%)	< 0.1%
Missing	9232
Missing (%)	19.0%
Memory size	380.4 KiB

1.0	27076
0.0	12370

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1.0
2nd row	1.0
3rd row	1.0
4th row	1.0
5th row	1.0

Common Values

Value	Count	Frequency (%)
1.0	27076	55.6%
0.0	12370	25.4%
(Missing)	9232	19.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1.0	27076	68.6%
0.0	12370	31.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Place_of_delivery
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	2
Distinct (%)	< 0.1%
Missing	6389
Missing (%)	13.1%
Memory size	380.4 KiB

0.0	35435
1.0	6854

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	0.0
3rd row	0.0
4th row	0.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	35435	72.8%
1.0	6854	14.1%
(Missing)	6389	13.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	35435	83.8%
1.0	6854	16.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Contraceptive_use
Categorical

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	380.4 KiB

0.0	39197
1.0	9481

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	0.0
3rd row	0.0
4th row	0.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	39197	80.5%
1.0	9481	19.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	39197	80.5%
1.0	9481	19.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Mediaexposure
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	2
Distinct (%)	< 0.1%
Missing	5458
Missing (%)	11.2%
Memory size	380.4 KiB

0.0	27332
1.0	15888

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0.0
2nd row	0.0
3rd row	0.0
4th row	0.0
5th row	0.0

Common Values

Value	Count	Frequency (%)
0.0	27332	56.1%
1.0	15888	32.6%
(Missing)	5458	11.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0.0	27332	63.2%
1.0	15888	36.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	60
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	27.39631867

Minimum	0
Maximum	59
Zeros	2259
Zeros (%)	4.6%
Negative	0
Negative (%)	0.0%
Memory size	380.4 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	11
median	27
Q3	43
95-th percentile	56
Maximum	59
Range	59
Interquartile range (IQR)	32

Descriptive statistics

Standard deviation	17.98561501
Coefficient of variation (CV)	0.6564975109
Kurtosis	-1.269167207
Mean	27.39631867
Median Absolute Deviation (MAD)	16
Skewness	0.06578261135
Sum	1333598
Variance	323.4823473
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	2259	4.6%
1	1158	2.4%
12	1081	2.2%
6	1076	2.2%
2	1052	2.2%
24	1047	2.2%
3	1024	2.1%
25	961	2.0%
36	942	1.9%
49	936	1.9%
Other values (50)	37142	76.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2259	4.6%
1	1158	2.4%
2	1052	2.2%
3	1024	2.1%
4	924	1.9%
5	905	1.9%
6	1076	2.2%
7	910	1.9%
8	840	1.7%
9	808	1.7%

Value	Count	Frequency (%)
59	615	1.3%
58	566	1.2%
57	605	1.2%
56	700	1.4%
55	654	1.3%
54	764	1.6%
53	808	1.7%
52	872	1.8%
51	916	1.9%
50	899	1.8%

dead_alive
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	380.4 KiB

1	44690
0	3988

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	44690	91.8%
0	3988	8.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	44690	91.8%
0	3988	8.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	Region	Rural_Urban	No_of_Children_under_5	Age_at_1st_Birth	BirthOrder	Prec.BirthInterval	TotalChildren	HighestEducation	SexofChild	childSize	WealthQuantile	prev_termin_pregnancy	Year	HandwashFascility	age	dead_alive
0	1	2	1	23	1	NaN	1	1	1	5.0	NaN	0.0	1	1.0	25.0	1
1	1	2	2	16	8	28.0	7	0	1	5.0	NaN	0.0	1	NaN	12.0	1
2	1	2	2	16	7	37.0	7	0	2	3.0	NaN	0.0	1	NaN	40.0	1
3	1	2	2	15	7	26.0	5	0	2	1.0	NaN	0.0	1	NaN	16.0	1
4	1	2	2	15	6	29.0	5	0	1	3.0	NaN	0.0	1	NaN	42.0	1
5	1	2	0	16	7	42.0	4	0	1	4.0	NaN	1.0	1	1.0	0.0	0
6	1	2	2	16	4	40.0	4	0	2	5.0	NaN	0.0	1	NaN	11.0	1
7	1	2	2	16	3	40.0	4	0	1	3.0	NaN	0.0	1	NaN	51.0	1
8	1	2	1	21	1	NaN	1	0	2	4.0	NaN	0.0	1	1.0	6.0	1
9	1	2	1	18	3	47.0	2	0	2	3.0	NaN	0.0	1	1.0	17.0	1

Last rows

	Region	Rural_Urban	No_of_Children_under_5	Age_at_1st_Birth	BirthOrder	Prec.BirthInterval	TotalChildren	HighestEducation	SexofChild	childSize	WealthQuantile	prev_termin_pregnancy	Year	Toiletfascility	Place_of_delivery	Contraceptive_use	Mediaexposure	age	dead_alive
48668	11	2	2	21	5	23.0	6	0	1	NaN	2.0	NaN	5	0.0	NaN	0.0	1.0	25.0	1
48669	11	2	2	21	4	25.0	6	0	2	NaN	2.0	NaN	5	0.0	NaN	0.0	1.0	48.0	1
48670	11	2	2	16	2	32.0	2	1	1	NaN	2.0	NaN	5	0.0	NaN	0.0	NaN	2.0	1
48671	11	2	2	16	1	NaN	2	1	1	NaN	2.0	NaN	5	0.0	NaN	0.0	NaN	35.0	1
48672	11	2	1	15	4	74.0	4	0	1	NaN	3.0	NaN	5	0.0	NaN	1.0	1.0	48.0	1
48673	11	2	2	13	4	14.0	4	0	1	NaN	2.0	NaN	5	1.0	NaN	0.0	NaN	17.0	1
48674	11	2	2	13	3	40.0	4	0	1	NaN	2.0	NaN	5	1.0	NaN	0.0	NaN	32.0	1
48675	11	2	1	18	4	44.0	5	1	2	NaN	1.0	NaN	5	0.0	NaN	0.0	NaN	35.0	1
48676	11	2	2	16	5	20.0	5	0	2	NaN	2.0	NaN	5	1.0	NaN	0.0	NaN	25.0	1
48677	11	2	2	16	4	32.0	5	0	2	NaN	2.0	NaN	5	1.0	NaN	0.0	NaN	45.0	1

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script